Magnus Johnsson
Papers
15
Total Citations
240
H-Index
9
About
Magnus Johnsson is a robotics and cognitive science researcher whose work sits at the intersection of artificial intelligence, haptic perception, and bio-inspired computing. Based at Lund University Cognitive Science (LUCS), Johnsson has dedicated much of his career to understanding and replicating the sense of touch in robotic systems, a challenging and underexplored frontier in robotics research. His most significant contributions center on the development of the LUCS Haptic Hand series — a progression of anthropomorphic robotic hands designed to model human neurophysiology computationally. Using self-organizing maps (SOMs) and tensor product representations, Johnsson pioneered systems capable of extracting meaningful features from haptic and proprioceptive sensory data, earning him over 34 citations for his landmark 2011 review alone. His neural network models of haptic shape perception (36 citations) further demonstrate his ability to bridge neuroscience and engineering. Beyond touch, Johnsson developed Ikaros, a cognitive modeling framework for robots that has garnered 41 citations — his most influential work — and explored motion estimation using growing neural gas techniques. With a body of work accumulating over 200 citations, Johnsson remains an important voice in bio-inspired robotics and embodied artificial cognition.
Research Focus
Key Achievements
Top Papers
- 1Ikaros: Building cognitive models for robots41 citations · 2009
- 2Neural network models of haptic shape perception36 citations · 2007
- 3Sense of Touch in Robots With Self-Organizing Maps34 citations · 2011
- 4Using GNG to improve 3D feature extraction—Application to 6DoF egomotion25 citations · 2012
- 5
- 6Experiments with artificial haptic perception in a robotic hand17 citations · 2006
- 7A Haptic System for the Lucs Haptic Hand I13 citations · 2005
- 8LUCS Haptic Hand II10 citations · 2006
- 9Haptic Perception with a Robotic Hand9 citations · 2006
- 10Experiments with Haptic Perception in a Robotic Hand8 citations · 2005